WO2016076746A1

WO2016076746A1 - Real-time and post-job design optimization workflows

Info

Publication number: WO2016076746A1
Application number: PCT/RU2014/000864
Authority: WO
Inventors: Artem Valerievich Kabannik; Denis Yurievich Emelyanov; Ivan Vladimirovich VELIKANOV; Mohan Kanaka Raju PANGA; Sergey Vladimirovich SEMENOV; Kira Vladimirovna YUDINA; Fedor Nikolaevitch LITVINETS
Original assignee: Schlumberger Canada Limited; Services Petroliers Schlumberger; Schlumberger Technology Corporation; Schlumberger Holdings Limited; Schlumberger Technology B.V.; Prad Research And Development Limited
Priority date: 2014-11-13
Filing date: 2014-11-13
Publication date: 2016-05-19

Abstract

A method of producing hydrocarbons from a well includes optimizing a treatment schedule of a hydraulic fracturing process to be performed on a well based on a formation model that predicts a modification to a geological formation based by a hydraulic fracturing process and a modified formation model, based on the modification,that predicts production of a well. The method may further include updating a treatment schedule of the hydraulic fracturing process to form an updated treatment schedule, performing a hydraulic fracturing process on a well based on the updated treatment schedule to form a fractured well, and producing hydrocarbons from the fractured well.

Description

REAL-TIME AND POST-JOB DESIGN OPTIMIZATION WORKFLOWS

BACKGROUND

Hydrocarbon fluids such as oil and natural gas are obtained from a subterranean geologic formation, commonly referred to as a reservoir, by drilling a well that penetrates the hydrocarbon-bearing formation. Once a wellbore is drilled, various forms of well completion components may be installed in order to control and enhance the efficiency of producing the various fluids from the reservoir.

FIG. 1 shows a diagram of a well (100) near a geological formation (103). The well includes surface structures (101) such as a derrick and tanks for holding fluids. The well (100) further includes a borehole (102) and various completion components (104) such as bridge plugs or packers that isolate the borehole (102) into sections (105)-(107). A casing may also be present to strengthen the walls of the borehole. By isolating individual sections (105)-(107) of the well, each section (105)-(107) may be independently operated which in some cases enhances the production of hydrocarbon fluids.

Improvement of operations performed on individual sections (105)-(107) of the well may improve the production of the well.

SUMMARY In one aspect, a method of optimizing a treatment schedule of a hydraulic fracturing process including a test set of treatment parameters may include predicting, based on a formation model of a geological formation, a modification of the geological formation by the hydraulic fracturing process and at least one quantification parameter associated with the hydraulic fracturing process. The method may further include forming a modified formation model based on the formation model and the predicted modification of the geological formation. The method may further include predicting at least one production indicator based on the modified formation model associated with the hydraulic fracturing process. The method may further include calculating a new set of treatment parameters based on the at least one quantification parameter, the at least one production indicator, and a numerical minimization process and setting the test set of treatment parameters to the new set of treatment parameters. The method may include repeating the above until the at least one production indicator attains a predetermined value. The method may further include optimizing the treatment schedule by setting at least one treatment schedule parameter based on the new set of treatment parameters.

In one aspect, a non-transitory computer readable medium (CRM) storing instructions for optimizing a treatment schedule of a hydraulic fracturing process including a test set of treatment parameters may include functionality for predicting, based on a formation model of a geological formation, a modification of the geological formation by the hydraulic fracturing process and at least one quantification parameter associated with the hydraulic fracturing process. The non- transitory CRM storing instructions may further include functionality for forming a modified formation model based on the formation model and the predicted modification of the geological formation. The non-transitory CRM storing instructions may further include functionality for predicting at least one production indicator based on the modified formation model associated with the hydraulic fracturing process. The non-transitory CRM storing instructions may further include functionality for calculating a new set of treatment parameters based on the at least one quantification parameter, the at least one production indicator, and a numerical minimization process and setting the test set of treatment parameters to the new set of treatment parameters. The non-transitory CRM storing instructions may further include functionality for repeating the above until the at least one production indicator attains a predetermined value. The non- transitory CRM storing instructions may further include functionality for optimizing the treatment schedule by setting at least one treatment schedule parameter based on the new set of treatment parameters.

In one aspect, a method of producing hydrocarbons from a well includes optimizing a treatment schedule of a hydraulic fracturing process to be performed on a well based on a formation model that predicts a modification to a geological formation based by a hydraulic fracturing process and a modified formation model, based on the modification, that predicts production of a well. The method may further include updating a treatment schedule of the hydraulic fracturing process to form an updated treatment schedule, performing a hydraulic fracturing process on a well based on the updated treatment schedule to form a fractured well, and producing hydrocarbons from the fractured well.

Other aspects and advantages of the disclosure will be apparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Certain embodiments of the disclosure will hereafter be described with reference to the accompanying drawings. It should be understood, however, that the accompanying drawings illustrate the various implementations described herein and are not meant to limit the scope of various technologies described herein. The drawings show and describe various embodiments of the current disclosure.

FIG. 1 shows an example of a well near a geological structure.

FIGs. 2(A)-(B) show an example treatment schedule and the result of a hydraulic fracturing process following the example treatment schedule.

FIGs. 3(A)-(C) show a method for optimizing a treatment schedule and an example of identified treatment parameters in accordance with one or more embodiments.

FIGs. 4(A)-(C) show a method for updating a treatment schedule, an example of an updated treatment schedule, and an example of the result of a hydraulic fracturing process following the updated treatment schedule in accordance with one or more embodiments.

FIG. 5 shows a system for optimizing a treatment schedule in accordance with one or more embodiments.

FIG. 6 shows a computing system in accordance with one or more embodiments.

FIG. 7 shows a two dimensional optimization result in accordance with one or more embodiments. FIGs. 8(A)-(C) show a four dimensional optimization result in accordance with one or more embodiments.

DETAILED DESCRIPTION Specific embodiments will now be described with reference to the accompanying figures. In the following description, numerous details are set forth as examples. It will be understood by those skilled in the art that one or more embodiments may be practiced without these specific details and that numerous variations or modifications may be possible without departing from the scope. Certain details known to those of ordinary skill in the art are omitted to avoid obscuring the description.

Hydraulic fracturing is a method of modifying a geological formation, typically through the use of pressurized liquids. Modification of the geological formation may increase the production of hydrocarbon fluids from a well within the geological formation. For example, a hydraulic fracturing process may create fractures that extend from a wellbore into a geological formation. These fractures allow hydrocarbon fluids to flow into the wellbore and, in turn, be extracted from the well. However, performing a hydraulic fracturing procedure is costly. Time, energy, materials, and equipment are used to perform the procedure. Accordingly, modifying a geological formation to increase production of a well may not be a good investment.

A hydraulic fracturing process is performed in accordance with a treatment schedule.

The treatment schedule is a list of segments to be performed in a sequence. Each segment of the treatment schedule includes a set of treatment parameters that quantify various aspects of the segment. If a well contains multiple sections, as depicted in FIG. 1 , the treatment schedule may contain segments directed toward each section.

An example treatment schedule (200) as known in the art is shown in FIG. 2(A). The example treatment schedule includes six segments that are to be applied to a first section (105) and second section (106) of a well (100). Each segment describes the purpose of the segment, for example a perforation segment or a pumping segment, and also includes a set of treatment parameters. For example, the treatment parameters of Segment 1 include the size and shape of the perforations, the number of perforations per grouping, and the spacing between groupings. As another example, Segment 2 clarifies that it is a pumping segment, the volume and rate of the pumping, and the fluid to be pumped. The example treatment schedule (200) as shown in FIG. 2(A) is merely a simple example and one of ordinary skill in the art would recognize a treatment schedule in practice may be much more complex, include many more segments, and include additional treatment parameters in each segment.

In one or more embodiments, treatment parameters are conventional hydraulic fracturing parameters including the rate of fluid injection, volume of fluid injection, and concentration of proppant in a fluid.

In one or more embodiments, other forms of hydraulic fracturing, such as a heterogeneous proppant placement hydraulic fracturing process or HiWAY®, may be optimized. In heterogenous proppant placement, additional segments are added to a hydraulic fracturing treatment schedule. These additional segments cause proppant rich fluids and clean fluids to be pumped in alternating pulses. By alternating the pumped fluids, the conductivity of fractures is improved by reducing blockage in the fractures caused by proppant. HIWAY® creates higher conductivity fractures and reduces the consumption of proppant. In one or more embodiments, treatment parameters are heterogeneous proppant placement hydraulic fracturing parameters including the PAD volume to treatment volume ratio, the SW:XL ratio, and the flush volume. In one or more embodiments, the proppant pulse duration and tail-in duration are treatment parameters. The proppant pulse duration is the length of time, during a HiWAY® fracturing process, that a proppant rich fluid is pumped when alternating between pumping a clean fluid and a proppant rich fluid. The tail- in duration is the duration of the last pumping of a proppant rich fluid during a HiWAY® fracturing process. The tail-in duration may be substantially longer than a proppant pulse duration and prevents pinchpoints or unsupported areas of the fracture near the wellbore. The PAD volume is the quantity of a fluid, without proppant, pumped into a wellbore that initiates fractures. For example, Segment 2 of FIG. 2(A) may initiate fractures in the formation. Once fractures have formed, proppant laden fluids and clean fluids are alternately pumped into the wellbore. The volume of the fluids pumped once fractures have been formed is the treatment volume. Thus, the PAD volume to treatment ratio is the volume of fluids pumped that cause fracture to the volume of fluids pumped

The SW:XL ratio is a parameter of a hydraulic fracturing process that includes both conventional and HiWAY® specific segments. Between the conventional segments and HiWAY® specific segments, a special segment is included in the hydraulic fracturing treatment plan that instructs a fluid containing a cross linked polymer gel to be pumped into the well. The quantity of fluids pumped without a cross-linked polymer gel is referred to as the Slick Water (SW) volume. The volume of pumped fluids containing the crosslinked polymer gel is referred to as the Crosslinked Gel (XL) volume. Thus, the SW:XL ratio is the volume of pumped slick water to the volume of pumped crosslinked gel durimng fracturing operation.

The flush volume is a quantity of a clean fluid pumped into the wellbore, after the geological formation has been fractured and a proppant has been pumped into the fracture to maintain the conductivity of the fracture, that flushes residual proppant, left over from proppant pumping segments, out of the wellbore and subsurface equipment. The flush volume may be greater than the total well volume if fractures in the geological formation are highly conductive or less than the total well volume if sections of the well are isolated.

In one or more embodiments, the perforation strategy is a treatment parameter. A perforation strategy may include the hole diameter of a perforation, the grouping of the perforations which is commonly defined in terms of the shots per foot and cluster length, and the spacing between groups which is commonly referred to as the cluster spacing.

As part of a hydraulic fracturing process, a number of perforations extending through the wellbore and into the geological formation are created. These perforations selectively control the interaction of fluids, pumped into a wellbore as part of a hydraulic fracturing process, with the geological formation. The number, spacing, depth, and placement of the perforations are referred to as a perforation strategy. For example, Segment 1 and Segment 4 as shown in FIG. 2(A) may be a perforation strategy A perforation strategy, as is known in the art, is selected heuristically based on regional knowledge such as the success of hydraulic fracturing of nearby wells. As seen in FIG. 2(A), the perforation strategy in the example treatment schedule (200) is identical for a first section (105) and a second section (106).

Additionally, as part of a hydraulic fracturing process, a number of fluids are pumped into a wellbore. The pumped fluids modify the geological formation. The pumping segments and associated treatment parameters for each of the pumping segments are referred to as a pumping strategy. For example, Segments 2-3 and 5-6 as shown in FIG. 2(A) may be a pumping strategy. Like a perforation strategy, a pumping strategy, as is known in the art, is selected heuristically based on regional knowledge such as the success of hydraulic fracturing of nearby wells. As seen in FIG. 2(A), the pumping strategy in the example treatment schedule (200) is identical for a first section (105) and a second section (106).

As discussed above, the treatment schedule (200) includes a perforation strategy and pumping strategy that were chosen heuristically. However, heuristically choosing a perforation strategy, pumping strategy, and then creating a treatment schedule as known in the art may be inefficient, e.g. production improvements of a well may be minimal and the cost of the hydraulic fracturing treatment may be high. Geological formations are highly variable and heuristically gained knowledge from nearby wells may not be applicable to nearby wells. In turn, application of heuristically gained knowledge may not maximize production and minimize cost due to variations in the geological formation.

To clarify this point, FIG. 2(B) shows a diagram of the potential result of applying a hydraulic fracturing process in accordance with the example treatment schedule (200) to a geological formation (103). The hydraulic fracturing process was performed according to the example treatment schedule (200). As shown in the example treatment schedule (200), the first section (105) was perforated and then pumped to modify the geological formation (103). As also shown in the example treatment schedule (200), the second section (106) was perforated and pumped to modify the geological formation (103).

When hydraulic fracturing process was applied to the first section (105), a number of fractures (201) extending from the borehole (102) into the geological formation (103) with high fluid conductivity were created. The high hydraulic conductivity and extent of the created fractures (201) enables hydrocarbon baring fluids to be more easily extracted from the geological formation (103) which may be desirable. However, when the hydraulic fracturing process was applied to the second section (106), the created fractures (202) partially extends into a portion of the geological formation (103). Due to the limited extent of the created fractures (202), extraction of hydrocarbon baring fluids near the second section (106) was slightly enhanced which may be undesirable. Thus, performing a hydraulic fracturing process according to a treatment schedule derived from a heuristically developed perforation strategy and pumping strategy as known in the art may be inefficient.

In view of the aforementioned points, methods of optimizing a treatment schedule of a hydraulic fracturing process have been developed. The methods do not depend on heuristically developed perforation strategies or pumping strategies as known in the art. Instead, a method that incorporates geological formation data, potential hydraulic fracturing procedures, predicted modification of the geological formation due to the potential hydraulic fracturing procedures, and predicted production of hydrocarbon baring fluids and gasses has been developed that systematically modifies the treatment schedule to maximize production and minimize cost.

In general, embodiments relate to a method, a system, and a non-transitory computer readable medium for optimizing a hydraulic fracturing process. In one or more embodiments, the optimization method maximizes increases production of a well due to a hydraulic fracturing process while minimizing the cost of performing the hydraulic fracturing process.

FIG. 3A shows a flowchart (300) of a method according to one or more embodiments. The method depicted in FIG. 3A may be used to optimize a hydraulic fracturing process in accordance with one or more embodiments. One or more items shown in FIG. 3A may be omitted, repeated, and/or performed in a different order among different embodiments.

At 3000, a goal for at least one production indicator is set for optimization. In one or more embodiments, the goal is set by a first computer program accepting the goal as an input. In one or more embodiments, a production indicator is the productivity index (PI), e.g. the oil flow rate divided by the difference between the reservoir pressure and the bottom-hole flowing pressure, or net present value (NPV) of the well, e.g. total future production estimate divided by total cost of future production estimate. The aforementioned production indicators are merely intended as examples and one skilled in the art may identify other production indicators.

At 3010, at least one static geological formation data describing a geological formation is obtained. In one or more embodiments, the geological formation is obtained by a first program accepting a geological formation data as input. In one or more embodiments, the static geological formation data may be a value or a data file. For example, a static geological formation data may be microseismic data, closure stress measurements, mini fall off analysis, pressure decline analysis, or a log interpretation contained in a data file. In one or more embodiments, the static geological formation data may be a measurement of a quantification parameter of a previously performed hydraulic fracturing process such as the proppant placement by chemical tracers pumped with the proppant, e.g. determining if flowback fluid returns through channels or the proppant. In one or more embodiments, the static geological formation data may be a measurement of the stimulation effectiveness of a previously performed hydraulic fracturing process by radioactive tracers, e.g. determining flow profile of the well.

At 3020, a static formation model of the geological formation model is created based on the at least one static geological formation data. In one or more embodiments, the static formation model is created by sending the at least one static geological formation data in a message from a first program to a second program, creating the static formation model in the second program, and then receiving by the first program a message from the second program indicating the static formation model has been created. In one or more embodiments, the second program is a fracture design program. In one or more embodiments, the fracture design program is Petrel®. In one or more embodiments, Petrel® includes the plugin Mangrove® which is a fracture design plugin. In one or more embodiments, the static formation model predicts how the geological formation model will be modified by a hydraulic fracturing process.

In one or more embodiments, if geological formation data is not obtainable items 3010 and 3020 are replaced by items 3014 and 3015. In 3014 and 3015, a basic static formation model of the well is created based on regional knowledge of the geological formation such as information gathered from nearby wells. Regional knowledge gained from nearby wells may include principal stresses in the geological formation, the structure of the geological formation, or other high level characteristics of the geological formation. Regional knowledge may include a static geological formation such as a microseismic data, closure stress measurements, mini fall off analysis, pressure decline analysis, or a log interpretation from an offset well, e.g. a nearby well that on the same geological formation. Regional knowledge may include a static geological formation data such as a measurement of a quantification parameter of a previously performed hydraulic fracturing process such as the proppant placement by chemical tracers pumped with the proppant from an offset well, e.g. determining if flowback fluid returns through channels or the proppant in the offset well. Regional knowledge may include a static geological formation data such as a measurement of the stimulation effectiveness of a previously performed hydraulic fracturing process by radioactive tracers from an offset well, e.g. determining flow profile of the offset well. Thus, the basic static formation model is created based on methods known in art. Using the basic static formation model, a section of the well is then subjected to a hydraulic fracturing process that modifies a portion of the geological formation. Production from the section of the well subjected to a hydraulic fracturing procedure is then begun.

Once production has begun, a static formation model is created based on production data from the well. The static formation model is created by parametrically modifying the basic static formation model until simulated production data based on the modified basic static formation model matches the measured production from the well. Thus, the created static formation model more accurately represents the interaction between the well and the geological formation than incorporation of heuristic information as is known in the art.

In one or more embodiments, if neither geological formation data nor production data are available, the basic static formation model is used as the created static formation model.

At 3030, sensitivity of the geological formation to a modification by a hydraulic fracturing process is identified based on the created static formation model. In one or more embodiments, each parameter of each set of treatment parameters in the treatment schedule is parametrically studied. Then, the sensitivity of the modification to each parameter is determined from the results of parametric study.

Parametrically studying the treatment parameters is further clarified by way of example. FIG. 3B shows the example treatment schedule (200) and annotations (301). The annotations (301) identify the treatment parameters that are to be studied parametrically. Each treatment parameter is individually varied and simulations of a hydraulic fracturing process for each variation is conducted. After each simulation, the resulting modification to the geological formation by the hydraulic fracturing process is quantified. Examples of quantification of the modification are the quantity of induced fractures, extent of the fractures, or conductivity of the fractures.

Parametrically studying the treatment parameters of the example treatment schedule (200) may start with first varying the diameter of the hole in Segment 1. For example, the hole diameter in Segment 1 may be set to 0.3 inches, a simulation of the hydraulic fracturing process according to the example treatment schedule (200) would then be carried out with the hole diameter set to 0.3 inches, and then the simulated modification would be quantified. Next the hole diameter may be set to 0.35 inches, a simulation of the hydraulic fracturing process according the example treatment schedule would then be carried out with the hole diameter set to 0.35 inches, and then the simulated modification would be quantified. Once a number of variations have been simulated, for example 10 variations, the quantification parameters would then be compared to identify sensitivity. If there was a large variation in the quantification parameters the hole diameters in Segment 1 would then be identified as a sensitive parameter. Conversely, if the quantification parameters had roughly the same value, the hole diameters in Segment 1 would not be identified as a sensitive parameter.

Once the sensitivity of the hole diameters in Segment 1 was identified, the next treatment parameter, in this example the grouping of the holes, would then be parametrically studied to identify sensitivities. Following the grouping treatment parameter, the spacing parameter would then be studied, and so on until the treatment parameters had been studied.

In one or more embodiments, the sensitivity of the geological formation to a modification by a hydraulic fracturing process is identified by sending a value of a parameter in a message from a first program to a second program, modifying the static formation model in the second program based on the parameter and predicting the modification of the geological formation by a hydraulic fracturing process, and receiving by the first program a message from the second program including a value representing a quantification parameter.

At 3040, a sensitive set of treatment parameters is selected based on the identified sensitivities. Item 3030 may identify that certain treatment parameters contained in the example treatment schedule (200) are sensitive. FIG. 3C show the example treatment schedule (200) with certain treatment parameters annotated as sensitive (302). In this example, the annotated as sensitive (302) treatment parameters would be selected.

FIG. 4A shows a flowchart (400) according to one or more embodiments. The method depicted in FIG. 4A may be used to optimize a hydraulic fracturing process in accordance with one or more embodiments. One or more items shown in FIG. 4A may be omitted, repeated, and/or performed in a different order among different embodiments.

At 4000, a modification, based on a static formation model, of the geological formation by a hydraulic fracturing process based on the test set of treatment parameters is predicted. In one or more embodiments, the test set of parameters is the sensitive set of treatment parameters determined in 3040. In one or more embodiments, the modification is predicted by sending the test set of treatment parameters in a message from a first program to a second program, computing the modification and at least one quantification parameter of the predicted modification in the second program, and then receiving by the first program a message from the second program including the predicted modification and one or more quantification parameters. In one or more embodiments, the second program is a fracture design program. In one or more embodiments, the fracture design program is Petrel®. In one or more embodiments, Petrel® includes the plugin Mangrove®. In one or more embodiments, the received quantification parameters are stored as an entry in a database and associated with the test set of treatment parameters. In one or more embodiments, a quantification parameter is the propping effectiveness, conductivity of the fracture, fracture dimensions, or efficiency index. In one or more embodiments, the efficiency index is the ratio of a productivity index of a HiWAY® treatment and a productivity index of a conventional hydraulic fracturing treatment with reference to a conventional schedule. In other words, the efficiency index is a ratio of the productivity index of a HiWAY® treatment to a productivity index of a conventional hydraulic fracturing treatment for a well.

At 4010, a hydrodynamic formation model, based on the static formation model and the predicted modification, is formed. In one or more embodiments, the hydrodynamic formation model is formed by sending the predicted modification and the static formation model in a message from the first program to a third program. In one or more embodiments, the third program is a reservoir semi-analytical or analytical simulator. In one or more embodiments, the reservoir numerical simulator is INTERSECT (IX) ®, ECLIPSE, UPM®, or Advanta. ®

At 4020, at least one production indicator is calculated based on the hydrodynamic formation model. In one or more embodiments, at least one production indicator is calculated by sending an instruction from the first program to the third program, in response to the received instruction the third program calculates the at least one production indicator based on the hydrodynamic formation model, and receiving in a message sent by the third program to the first program the at least one production indicator. In one or more embodiments, the received production indicators are stored as an entry in a data base and associated with the test set of treatment parameters. At 4030, the production indicators and quantification parameters are compared to a goal. In one or more embodiments, the goal is to simply maximize the production indicators or quantification parameters. If the goal is maximization, the current set of production indicators and quantification parameters are compared to the previous set. If the difference between the current set and previous set is less than a value set by a user, for example 0.01%, 0.1%, or 1%, the current set of production indicators and quantification parameters are determined as maximized and the current test set of treatment parameters are considered to be an optimized set of treatment parameters and the method continues to 4050. In one or more embodiments, the goal is a set of specific minimum values for each production indicator and quantification parameter. If the production indicators and quantification parameters meet or exceed the set of specific minimum values, the current test set of treatment parameters are considered to be an optimized set of treatment parameters and the method continues to 4050. If the production indicators and quantification parameters do not meet the goal, the method proceeds to 4040. In one or more embodiments, the goal is a set of predetermined values for each production indicator and quantification parameter. In one or more embodiments, the goal is a predetermined value for a combination of production indicators and quantification parameters. For example, the combination may be a linear combination such as adding the production indicators and quantification parameters together to form a composite production indicator. In one or more embodiments, the goal may be to find the maximum potential value of a composite production indicator.

At 4040, a new set of treatment parameters are computed based on the quantification parameters and production indicators, associated with sets of treatment parameters, and a numerical optimization process. In one or more embodiments, the quantification parameters and production indicators, associated with sets of parameters, are retrieved from entries in the database. In one or more embodiments, the numerical optimization process includes computing a quality of the test set of treatment parameters. Once computed, the quality of the test set of treatment parameters is recorded as an entry in the database. Then, based on the entries in the database, the new set of treatment parameters are computed by the numerical optimization process. Once the new set of treatment parameters is computed, the test set of treatment parameters is set to the new set of treatment parameters. The method then continues to 4000. In one or more embodiments, the numerical optimization process is a stochastic optimization algorithm. In one or more embodiments, the stochastic optimization algorithm is a genetic or neural network algorithm. In one or more embodiments, the numerical optimization process is a deterministic optimization algorithm. In one or more embodiments, the deterministic optimization algorithm is the simplex method or a mixed-integer nonlinear (MINLP) algorithm.

At 4050, a treatment schedule for a to-be-performed hydraulic fracturing process is updated based on the optimized set of treatment parameters. FIG. 4B shows an example of an updated treatment schedule (401) in accordance with one or more embodiments. As seen in the updated treatment schedule (401), the treatment parameters have been modified when compared to the example treatment schedule (200). In one or more embodiments, the to-be- performed hydraulic fracturing process is performed according to the updated treatment schedule. FIG. 4C shows an example of the result (900) of performing a hydraulic fracturing process on a well (100) according to the updated treatment schedule (401) in accordance with one or more embodiments. As seen, a first set of fractures (402) extending outward from the first section (105) and a second set of fractures (403) extending outward from the second section (106) were created. The created fractures extend throughout the geological formation (102), have good conductivity, and do not extend beyond the hydrocarbon baring region. Thus, by performing the hydraulic fracturing procedure according to the updated treatment schedule (401) the production of the well has been enhanced the cost of the hydraulic fracturing procedure has been minimized.

In one or more embodiments, the well performance is evaluated based on production data once the hydraulic fracturing process is completed. In one or more embodiments, well performance is evaluated by direct comparison of the production indicators with nearby wells completed conventionally, e.g. comparison of specific productivity indices of wells fractured using an optimized hydraulic fracturing schedule to wells fractured using a traditional hydraulic fracturing schedule. By directly comparing optimized wells to traditional wells, the quality of the optimized hydraulic fracturing treatment schedule may be determined. In one or more embodiments, well performance is evaluated based on flowback or production data once the hydraulic fracturing process is completed. In one or more embodiments, estimation of channeled half-length or channeled area from flowback or production data is computed. In one or more embodiments, estimation of heterogeneous proppant placement with chemical tracers, pumped with proppant is computed, e.g. the tracer concentration indicates if the flowback fluid goes through channels (low concentration or no tracers) or the proppant pack (with tracers). In one or more embodiments, estimation of stimulation effectiveness of different perforations (clusters) with radioactive tracers (flow profiles of the wells) is calculated, e.g. detecting fluid movements between fractures belonging to different clusters.

FIG. 5 shows a system (500) in accordance with one or more embodiments. Specifically, FIG. 5 shows a system of three programs. The first program (501) optimizes a treatment schedule of a well disposed on a geological formation according to the methods shown in FIGs. 3 and 4. The first program (501) includes a database (502) for storing production indicators and quantification parameters associated with sets of treatment parameters.

In one or more embodiments, the first program (501 ) also includes a numerical optimizer (503) that selects test sets of treatment parameters. Test sets of treatment parameters are selected based on the entries in the database (502).

In one or more embodiments, the firsts program (501) includes a GUI (505) for interacting with a user, e.g. to receive input or select data files such as geological formation data. In one or more embodiments, the GUI (505) has one or more widgets (e.g. drop down lists, text boxes, radio buttons, etc.) used to interact with a user.

In one or more embodiments, the first program (501) includes a messaging routine (504) for sending and receiving data and instructions to and from other programs. The messaging routine (504) may send messages to other programs running on the same system as the first program (501) or to programs running on other systems. For example, the first program (501) may be running on a first computer at a first geographic location and may send messages to a second program running on a second computer at a second geographic location via the internet.

In one or more embodiment, the second program (510) is a fracture design application. The second program (510) includes a fracture simulator (51 1 ) that predicts the modification of a geological formation due to a hydraulic fracturing process based on a set of treatment parameters. The second program (510) also includes a messaging routine (512) that receives data and commands from other programs and sends data to other programs, such as the first program (501). For example, the messaging routine (512) may receive instructions to execute a prediction from the first program (501 ) and send results of the prediction to the first program (501). The messaging routine (512) may send message to other programs running on the same system as the second program (510) or to programs running on other systems.

In one or more embodiment, the third program (520) is a reservoir numerical simulator. The third program (520) includes a production simulator (521) that predicts the production of the well. The production simulator (521) also calculates production indicators for the predicted production. The third program (520) additionally includes a messaging routine (522) that receives data and commands from other programs and sends data to other programs, such as the first program (501). For example, the messaging routine (522) may receive data from a first program (501) that is used by the production simulator (521) to predict the production of the well. The messaging routine (522) may send message to other programs running on the same system as the second program (520) or to programs running on other systems.

In another embodiment, the second program (510) and third program (520) are modules within the first program (501).

Embodiments may be implemented on virtually any type of computing system, regardless of the platform being used. For example, the computing system may be one or more mobile devices (e.g., laptop computer, smart phone, personal digital assistant, tablet computer, or other mobile device), desktop computers, servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments. For example, as shown in FIG. 6, the computing system (600) may include one or more computer processor(s) (602), associated memory (604) (e.g., random access memory (RAM), cache memory, flash memory, etc.), one or more storage device(s) (606) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick, etc.), and numerous other elements and functionalities. The computer processor(s) (602) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores, or micro-cores of a processor. The computing system (600) may also include one or more input device(s) (610), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. Further, the computing system (600) may include one or more output device(s) (608), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device. One or more of the output device(s) may be the same or different from the input device(s). The computing system (600) may be connected to a network (612) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) via a network interface connection (not shown). The input and output device(s) may be locally or remotely (e.g., via the network (612)) connected to the computer processor(s) (602), memory (604), and storage device(s) (606). Many different types of computing systems exist, and the aforementioned input and output device(s) may take other forms.

Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other non-transitory computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that when executed by a processor(s), is configured to perform embodiments. Further, one or more elements of the aforementioned computing system (600) may be located at a remote location and connected to the other elements over a network (612). Further, one or more embodiments may be implemented on a distributed system having a plurality of nodes, where each portion such as the first program, second program, and the third program may be located on a different node within the distributed system. For example, the first program (501) may send messages to multiple second programs (510) or third program (520) located on the distributed system. Each second program (510) or third program (520) would be issued separate instructions to parallelize the optimization. In one embodiment, the node corresponds to a distinct computing device. In another embodiment, the node may correspond to a computer processor with associated physical memory. The node may alternatively correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

Eagle Ford Example

The methods of FIG. 3 and 4 were tested on a geological formation in South Texas, USA. The geological formation is referred to as the Eagle Ford Shale. A horizontal well located on the geological formation was selected as a test case for optimization of a hydraulic fracturing process in accordance with one or more embodiments.

The optimization goal was set to maximize the cumulative natural gas production after one and a half years. A static formation model of the well was constructed in Petrel® using existing geological formation data. The model was parametrically studied to identify which hydraulic fracturing parameters impacted the modification of the geological formation. The pulse duration, pumping rate, proppant concentration, and SW:XL ratio were identified as sensitive.

In a first optimization process, the optimization was restricted to pulse duration and pumping rate. An optimized pulse time of 12 s and pumping rate of 70 bbl/min were calculated using the optimization process shown in FIGs. 3 and 4. To verify the accuracy of the optimization process, the value of the goal was parametrically studied. To perform the parametric study, each parameter, e.g. the pump rate and pulse time, was varied in increments and the cumulative gas was calculated. The pumping rate was varied from 20-80 bbl/min and the pulse time was varied from 9-16 seconds. The results of the parametric study are shown in FIG. 7. As seen from FIG. 7, the global maximum (700) is at a pulse time of 12 s and pumping rate of 70 bbl/min (two-parameter optimization).

In a second optimization process, the pulse duration, pumping rate, proppant concentration, and SW:XL ratio were optimized. An optimized pulse time of 10 seconds, pumping rate of 40 bbl/min, proppant concentration of 7, and SW:XL ratio of 0: 1 were calculated using the optimization process shown in FIGs. 3 and 4. To verify the accuracy of the optimization process, the value of the goal was parametrically studied. To perform the parametric study, each parameter— e.g. the pulse duration, pumping rate, proppant concentration, and SW:XL ratio—was varied in increments and the cumulative gas was calculated. The pumping rate was varied from 40-80 bbl/min, the pulse time was varied from 10-16 seconds, the proppant concentration was varied from 4-8, and the SW:SL ratio was varied from 0:1-2:1.

The results of the parametric study are shown in FIGs. 8A-8C. Specifically, FIG. 8 A shows the result of varying the pulse time, pumping rate, and proppant concentration while holding the SW:XL ratio constant at 2:1; FIG. 8B shows the result of varying the pulse time, pumping rate, and proppant concentration while holding the S W:XL ratio constant at 1 : 1 ; and FIG. 8C shows the result of varying the pulse time, pumping rate, and proppant concentration while holding the SW:XL ratio constant at 0: 1. As seen from FIG. 8C, the global maximum (circled and labeled as n=4, wherein number 4 stands for four-parametric optimization) is located at the optimized global maximum for cumulative gas production.

The method of optimizing a treatment schedule of a hydraulic fracturing process according to one or more embodiments may provide one or more of the following advantages. The method may maximize production indicators of a well by minimizing the cost of a hydraulic fracturing process while maximizing production. The method of optimizing a treatment schedule of a hydraulic fracturing process according to one or more embodiments may be automated which reduces the time for optimization. While the above has been described with respect to a limited number of embodiments, those skilled in the art, having the benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope as disclosed herein. Accordingly, the scope should be limited by the attached claims.

Claims

CLAIMS What is claimed is:

1. A method of optimizing a treatment schedule of a hydraulic fracturing process comprising a test set of treatment parameters, the method comprising:

a) predicting, based on a formation model of a geological formation:

a modification of the geological formation by the hydraulic fracturing process: at least one quantification parameter associated with the hydraulic fracturing process;

b) forming a modified formation model based on the formation model and the predicted modification of the geological formation;

c) predicting at least one production indicator based on the modified formation model associated with the hydraulic fracturing process;

d) calculating a new set of treatment parameters based on the at least one quantification parameter, the at least one production indicator, and a numerical minimization process and setting the test set of treatment parameters to the new set of treatment parameters;

e) repeating (a) through (d) until the at least one production indicator attains a predetermined value.

f) optimizing the treatment schedule by setting at least one treatment schedule parameter based on the new set of treatment parameters.

2. The method of claim 1 , further comprising:

selecting at least one production indicator for optimization;

obtaining at least one static geological formation data of the geological formation; and modifying the formation model of the geological formation based on the at least one static geological formation data;

identifying, based on the formation model, the sensitivity of a modification of the geological formation by the hydraulic fracturing process; and

selecting, based on the identified sensitivity, the test set of treatment parameters.

3. The method of claim 1 , wherein the quantification parameter is selected from the group containing the propping effectiveness, conductivity of the fracture, fracture dimensions, or efficiency index.

4. The method of claim 1 , wherein the production indicator is the Productivity Index (Pi) or Net Present Value (NPV) of the well.

5. The method of claim 1 , wherein the numerical minimization process is a deterministic algorithm.

6. The method of claim 2, wherein the at least one static geological formation data is selected from the group containing microseismic data, closure stress measurements, mini fall off analysis, pressure decline analysis, and a log interpretation.

7. The method of claim 2, wherein the at least static geological formation data is derived from measured production data by parametrically modifying the formation model until a simulated production data based on the modified formation model matches the measured production data.

8. A non-transitory computer readable medium (CRM) storing instructions for optimizing a treatment schedule of a hydraulic fracturing process comprising a test set of treatment parameters, the instructions comprising functionality for:

a) predicting, based on a formation model of a geological formation:

a modification of the geological formation by the hydraulic fracturing process; at least one quantification parameter associated with the hydraulic fracturing process;

9. The non-transitory CRM of claim 8, the instructions further comprising functionality for: selecting at least one production indicator for optimization;

10. The non-transitory CRM of claim 8, wherein the quantification parameter is selected from the group containing the propping effectiveness, conductivity of the fracture, fracture dimensions, or efficiency index.

1 1. The non-transitory CRM of claim 8, wherein the production indicator is the Productivity Index (PI) or Net Present Value (NPV) of the well.

12. The non-transitory CRM of claim 8, wherein numerical minimization process is a deterministic algorithm.

13. The non-transitory CRM of claim 9, wherein the at least static geological formation data is selected from the group containing microseismic data, closure stress measurements, mini fall off analysis, pressure decline analysis, and a log interpretation.

14. The non-transitory CRM of claim 9, wherein the at least static geological formation data is derived from measured production data by parametrically studying a static formation model until a simulated production data based on the static formation model matches the measured production data.

15. The non-transitory CRM of claim 9, wherein the modification and quantification parameter are predicted by sending the test set of parameters in a message from a first program to a second program, computing the modification and at least one quantification parameter of the predicted modification in the second program, and then receiving by the first program a message from the second program including the predicted modification and a quantification parameter.

16. The n on -transitory CRM of claim 1 5, wherein the formation model is formed by sending the predicted modification and the static formation model in a message from the first program to a third program.

17. The non-transitory CRM of claim 16, wherein the at least one production indicator is calculated by sending an instruction from the first program to the third program, in response to the received instruction the third program calculates the at least one production indicator based on the formation model, and receiving in a message sent by the third program to the first program the at least one production indicator.

18. Λ method of producing hydrocarbons from a well, the method comprising:

optimizing a treatment schedule of a hydraulic fracturing process to be performed on a well based on:

a formation model that predicts a modification to a geological formation based by a hydraulic fracturing process,

a modified formation model, based on the modification, that predicts production of a well,

updating a treatment schedule of the hydraulic fracturing process to form an updated treatment schedule,

performing a hydraulic fracturing process on a well based on the updated treatment schedule to form a fractured well, and

producing hydrocarbons from the fractured well.

19. The method of claim 18, wherein the first formation model is based on at least one static geological formation data;

20. The method of claim 18, wherein the at least static geological formation data is selected from the group containing microseismic data, closure stress measurements, mini fall off analysis, pressure decline analysis, and a log interpretation.